An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations

Automatic workload estimation has received much attention because of its application in error prevention, diagnosis, and treatment of neural system impairment. The development of a simple but reliable method using minimum number of psychophysiological signals is a challenge in automatic workload est...

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Veröffentlicht in:International journal of psychophysiology Jg. 110; S. 91 - 101
Hauptverfasser: Ghaderyan, Peyvand, Abbasi, Ataollah
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.12.2016
Schlagworte:
Adult
Arithmetic task
Cognitive load estimation
Data Interpretation, Statistical
Electrodermal activity
Executive Function - physiology
Female
Galvanic Skin Response - physiology
Humans
Machine learning
Male
Problem Solving - physiology
Psychomotor Performance - physiology
Signal Processing, Computer-Assisted
Support Vector Machine
Young Adult
Arithmetic task
Electrodermal activity
Cognitive load estimation
Support vector machine
Machine learning
ISSN:0167-8760, 1872-7697, 1872-7697
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Zusammenfassung:Automatic workload estimation has received much attention because of its application in error prevention, diagnosis, and treatment of neural system impairment. The development of a simple but reliable method using minimum number of psychophysiological signals is a challenge in automatic workload estimation. To address this challenge, this paper presented three different decomposition techniques (Fourier, cepstrum, and wavelet transforms) to analyze electrodermal activity (EDA). The efficiency of various statistical and entropic features was investigated and compared. To recognize different levels of an arithmetic task, the features were processed by principal component analysis and machine-learning techniques. These methods have been incorporated into a workload estimation system based on two types: feature-level and decision-level combinations. The results indicated the reliability of the method for automatic and real-time inference of psychological states. This method provided a quantitative estimation of the workload levels and a bias-free evaluation approach. The high-average accuracy of 90% and cost effective requirement were the two important attributes of the proposed workload estimation system. New entropic features were proved to be more sensitive measures for quantifying time and frequency changes in EDA. The effectiveness of these measures was also compared with conventional tonic EDA measures, demonstrating the superiority of the proposed method in achieving accurate estimation of workload levels. •An efficient workload estimation method based on EDA is presented.•The feasibility of a combined pattern recognition system is explored.•A set of new time and frequency features is extracted.•The proposed features are more suitable than conventional tonic EDA measures.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0167-8760
1872-7697
1872-7697
DOI:10.1016/j.ijpsycho.2016.10.013